CN107576982A - A kind of sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method - Google Patents

A kind of sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method Download PDF

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CN107576982A
CN107576982A CN201710778813.7A CN201710778813A CN107576982A CN 107576982 A CN107576982 A CN 107576982A CN 201710778813 A CN201710778813 A CN 201710778813A CN 107576982 A CN107576982 A CN 107576982A
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CN107576982B (en
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吴曲波
黄伟传
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Beijing Research Institute of Uranium Geology
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Beijing Research Institute of Uranium Geology
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Abstract

The invention belongs to uranium ore Comprehensive Seismic Prediction method and technology field, and in particular to a kind of sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method.The present invention uses question of seismic wave impedance inversion method inverse underground sand-body distribution situation, obtain the formation at target locations sand factor information for having direct relation with U metallogeny, destination layer wave impedance information is converted to the porosity information relevant with mineralization using the transformational relation or experience transformational relation of borehole data statistics simultaneously, finally utilize 3-d seismic data set extraction and the sensitive earthquake attribute information of U metallogeny environmental correclation, the U metallogeny potentiality in work area are predicted by this three category informations comprehensive analysis, it can reach quick, effectively predict the purpose of sandstone-type uranium mineralization with respect target reservoir development scope.

Description

A kind of sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method
Technical field
The invention belongs to uranium ore Comprehensive Seismic Prediction method and technology field, and in particular to a kind of sandstone-type uranium mineralization with respect earthquake synthesis Forecasting Methodology.
Background technology
The work of sandstone-type uranium mineralization with respect prediction and evaluation is a mission critical in exploration of sandstone type uranium deposits.Conventional geology Predicting Technique Mainly it is predicted using geologic survey, drilling data, shortcoming is that the cost of prediction is higher, the cycle is longer；Using geochemical The geophysical methods such as reconnoitre, radioactivity physical prospecting, electromagnetic method are predicted evaluation, its result often do not reach required precision, It is ineffective；And seismic exploration technique has preferable detection accuracy for Formation of Sandstone-type Uranium Deposits environment, seismic technology pair is used Sandstone-type uranium mineralization with respect target reservoir, which carries out integrated forecasting, has preferable prospect.
The content of the invention
The technical problem to be solved in the present invention is to provide a kind of sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method, this method can Quickly and efficiently predict the planar distribution scope of sandstone-type uranium mineralization with respect target reservoir.
In order to realize this purpose, the present invention adopts the technical scheme that：
A kind of sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method, this method comprise the following steps：
(1) in research area, a set of sandstone-type uranium mineralization with respect 3-D seismics pure wave data are gathered；
Field 3D seismic data is gathered, field 3D seismic data is handled successively to obtain 3-D seismics pure wave Data；
(2) Inversion Calculation is carried out to the 3-D seismics pure wave data of step (1) using based on modeling inversion, obtains three-dimensional Wave Impedance Data Volume, and then calculate three-dimensional lithology data body；
(3) the sand factor distributed data of destination layer position sand body is asked for, and is depicted as isogram；
The three-dimensional lithology data body obtained using step (2), mesh is calculated using the trace math modules of geoview softwares The sand factor data on stratum are marked, and draw plane equivalence；
(4) relation of porosity data and Acoustic Impedance Data is asked for
1. there are porosity data, sonic data, the situation of the class log data of density data three in area's log is studied Under：The intersection that porosity and wave impedance are carried out using the cross plot modules of excel softwares or geoview softwares is analyzed；
During analysis, the product that x transverse axis is Acoustic Impedance Data, Acoustic Impedance Data=sound wave and density data is set；The y longitudinal axis is Porosity data；Then fitted by the linear fit instrument of excel softwares or the cross plot modules of geoview softwares The change type y=ax+b, y of wave impedance turn hole porosity are porosity data, and x is Acoustic Impedance Data, and a, b are the ginseng for needing to be fitted Number；
2. in the case where studying the non-porous porosity log data in area：Using following formula (1), wave impedance is converted into hole Porosity data：
Wherein, ACSolid skeletalRepresent the interval transit time of rock solid skeleton, ACFluidBetween expression blowhole during the sound wave of fluid Difference, IMP are Acoustic Impedance Data, and POR is porosity data；
(5) transformational relation for the wave impedance turn hole porosity asked for using step (4), porosity is converted to by Acoustic Impedance Data Data, obtain formation at target locations porosity data distribution, and drawing isoline figure；
(6) 3-D seismics pure wave data are utilized, extract 3 kinds of earthquake RMS amplitude, instantaneous phase, arc length seismic properties, Cluster analysis is carried out to this three kinds of seismic properties, constraints is established based on borehole data, regression fit goes out a kind of new earthquake Combinations of attributes；
(7) above-mentioned formation at target locations sand factor data, porosity distributed data and seismic properties combination distribution are analyzed in intersection Data, integrated forecasting Formation of Sandstone-type Uranium Deposits beneficial zone；
Integrated forecasting Formation of Sandstone-type Uranium Deposits beneficial zone refers to numerical value in formation at target locations sand factor distribution map more than X's The region of region, porosity value more than Y, region of the seismic properties combined value more than Z carry out intersection analysis and research, carry out again accordingly Into the integrated forecasting of ore deposit beneficial zone.
Further, a kind of sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method as described above, in step (1), earthquake is passed through Instrument gather field 3D seismic data, the process handled field 3D seismic data be successively carry out static correction, denoising, Amplitude compensation, deconvolution, dynamic school superposition, migration processing.
Further, a kind of sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method as described above, in step (2), specific steps It is as follows：
1. the foundation of initial model：The well data in collection research area first, to sound wave curve therein and density curve It is smoothed and standardization；
2. establish inverting initial model using the STRATA modules of geoview softwares；
3. wave impedance inversion calculates：The 3-D seismics pure wave data obtained to step (1) carry out Inversion Calculation, obtain three-dimensional Wave Impedance Data Volume；
4. determine the threshold value of wave resistance anti-rotation lithology：The threshold value of the wave resistance anti-rotation lithology in each research area is not quite similar, The determination of threshold value is analyzed based on work area petrophysical parameter；
5. lithology data calculates：The threshold value of the wave resistance anti-rotation lithology 4. obtained based on step (2), by step (2) 3. To three-dimensional Wave Impedance Data Volume be converted to three-dimensional lithology data body, the form of three-dimensional lithology data body is LITH (x, y, t), its Middle x represents No. Inline of 3D seismic data, and y represents No. Xline of 3D seismic data, and t represents the time；
The value of LITH (x, y, t) data volume is 0 or 1,0 expression mud stone, and 1 represents sandstone.
Further, a kind of sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method as described above, step (2) 1. in, smooth place Use or 5 smoothing processings during reason at 3 points；
3 points of density data or the processing of 5 moving averages are respectively as shown by the following formula：
● 3 moving average formula of density：dden(i)=(dden(i-1)+dden(i)+dden(i+1))/3
Wherein, diRepresent the density value of some sampled point, di-1For the density value of the previous sampled point of the sampled point, di+1 For the density value of the latter sampled point of the sampled point；
● 5 moving average formula of density：dden(i)=(dden(i-2)+dden(i-1)+dden(i)+dden(i+1)+dden(i+2))/5
Wherein, diRepresent the density value of certain sampled point, di-2For the density value of the first two sampled point of the sampled point, di-1For The density value of the previous sampled point of the sampled point, di+1For the density value of the latter sampled point of the sampled point, di+2Adopted for this The density value of latter two sampled point of sampling point；
3 points of sonic data or the processing of 5 moving averages are respectively as shown by the following formula：
● 3 moving average formula of sound wave：dson(i)=(dson(i-1)+dson(i)+dson(i+1))/3
Wherein, diRepresent the sound wave value of some sampled point, di-1For the sound wave value of the previous sampled point of the sampled point, di+1 For the sound wave value of the latter sampled point of the sampled point；
● 5 moving average formula of sound wave：dson(i)=(dson(i-2)+dson(i-1)+dson(i)+dson(i+1)+dson(i+2))/5
Wherein, diRepresent the sound wave value of certain sampled point, di-2For the sound wave value of the first two sampled point of the sampled point, di-1For The sound wave value of the previous sampled point of the sampled point, di+1For the sound wave value of the latter sampled point of the sampled point, di+2Adopted for this The sound wave value of latter two sampled point of sampling point；
Step (2) 1. in, during standardization using the logging nomalize modules of geoview softwares at Reason, same codomain scope is normalized to by the sound wave of the well for establishing initial model and density data；
Step (2) 2. in, when establishing inverting initial model：
When carrying out Inversion Calculation using the STRATA modules of geoview softwares, the setting of inverted parameters is：10-15Hz High cut-off frequency；Iterative times 10~20 times；Sample rate is 1ms~2ms；Maximum resistance variation scope is 25%~50%；Prewhitening Rate is 1%；Computing block size is 1ms~2ms, and the computing block size is identical with sample rate；Scale factor is 1；
Inverting type is multiple tracks inverting, Inline directions 10~20, Xline directions 10~20；
Step (2) 4. in, the cross plot modules of petrophysical parameter analysis and utilization geoview softwares.
Further, a kind of sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method as described above, in step (3), specific steps It is as follows：
1. 3-D seismics pure wave data input Landmark seismic interpretation softwares are based on, the dimensionally number of plies that explanation is obtained According to importing in geoview softwares, the three-dimensional lithology data body of step (2) is imported into geoview softwares；
2. determining destination layer scope, using the trace math modules of geoview softwares in the range of the bottom of destination layer top, enter Row circle statistics calculate, and obtain stratum sand factor distributed data RATIO (x, y)；
3. using, " trace math " modules need to write code, and specific code is as follows：
Sand factor RATIO (x, y) distributed data of formation at target locations is can obtain accordingly；
4. to step (3) 2. in sand factor RATIO (x, y) data, carry out isopleth into figure.
Further, a kind of sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method as described above, in step (5), specific steps It is as follows：
1. the conversion relational expression obtained using step (4), is calculated using the trace math modules of geoview softwares Porosity 3D data volume POR (x, y, t), x represent No. Inline of 3D seismic data, and y represents 3D seismic data No. Xline, t represents the time；
2. determining destination layer scope, using the trace math modules of geoview softwares in the range of the bottom of destination layer top, enter Row circle statistics calculate, and obtain formation at target locations average pore distributed data POR_AVA (x, y), and x represents 3D seismic data No. Inline, y represents No. Xline of 3D seismic data；
The code write using trace math modules is as follows：
3. porosity POR_AVA (x, y) distributed data of formation at target locations is can obtain accordingly；
4. to step (5) 3. in obtained porosity POR_AVA (x, y) data, carry out isopleth into figure.
Further, a kind of sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method as described above, in step (6), by step (1) 3-D seismics pure wave data input determines the position of target reservoir into Landmark softwares, and in this position, each 20ms is thick up and down 3 kinds of extraction earthquake RMS amplitude, instantaneous phase, arc length seismic properties in degree, then this three kinds of seismic properties are carried out with cluster point Analysis；
Identify the more attribute constraint situations of different zones, the restraint condition in the practical application in Er'lian Basin sandstone-type uranium mining area For：Earthquake RMS amplitude property value is more than 25, instantaneous phase property value and is more than 0, arc length property value more than 7, returns intend accordingly A kind of data of new seismic properties combination are closed out, and are depicted as isogram accordingly.
Further, a kind of sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method as described above, in step (7), X, Y, Z value Choose according to the different and different of sandstone-type uranium mineralization with respect area；The method for obtaining X, Y, Z value is all industry by that will study in area Bore position projects into formation at target locations sand factor distribution map, porosity distribution map, seismic properties constitutional diagram respectively, reads all The sand factor value of bore position, porosity value, seismic properties combined value, then by these sand factor value, porosity value, seismic properties Combined value carries out arithmetic average, obtains X, Y, Z value.
Further, a kind of sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method as described above, in step (7), integrated forecasting Step is：
1. in the formation at target locations sand factor distribution map that step (3) obtains, region labeling of the codomain more than 0.8 is Favorable Areas A；
2. in the formation at target locations average pore distribution map that step (5) is obtained, region labeling of the codomain more than 12% is Favorable Areas B；
3. in the data isogram that the seismic properties that step (6) is obtained combine, codomain is more than 0.45 region labeling For Favorable Areas C；
4. overlapping above-mentioned tri- Favorable Areas of A, B, C using graphics software, the overlapping intersection area of three Favorable Areas of prediction is I Class ore prospect area；It is II classes into ore deposit Favorable Areas to have the overlapping intersection area of two panels Favorable Areas in three Favorable Areas of prediction；Other Situation does not give a forecast.
The beneficial effect of technical solution of the present invention is：The present invention uses question of seismic wave impedance inversion method inverse underground sand body point Cloth situation, the formation at target locations sand factor information for having direct relation with U metallogeny is obtained, while utilize the conversion of borehole data statistics Relation or experience transformational relation are converted to destination layer wave impedance information the porosity information relevant with mineralization, finally utilize 3-d seismic data set extracts the sensitive earthquake attribute information with U metallogeny environmental correclation, is ground by this three category informations comprehensive analysis Study carefully the U metallogeny potentiality in prediction work area, the mesh for quickly and efficiently predicting sandstone-type uranium mineralization with respect target reservoir development scope can be reached 's.
Embodiment
Technical solution of the present invention is described in detail with reference to specific embodiment.
A kind of sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method of the present invention, this method comprise the following steps：
(1) in research area, a set of sandstone-type uranium mineralization with respect 3-D seismics pure wave data are gathered；
Field 3D seismic data is gathered by seismic detector, field 3D seismic data handled successively to obtain three Tie up earthquake pure wave data；
The process handled field 3D seismic data is to carry out static correction, denoising, amplitude compensation, anti-pleat successively Product, dynamic school superposition, migration processing.
(2) Inversion Calculation is carried out to the 3-D seismics pure wave data of step (1) using based on modeling inversion, obtains three-dimensional Wave Impedance Data Volume, and then calculate three-dimensional lithology data body；
Comprise the following steps that：
1. the foundation of initial model：The well data in collection research area first, to sound wave curve therein and density curve It is smoothed and standardization；
Step (2) 1. in, use or 5 smoothing processings at 3 points during smoothing processing；
3 points of density data or the processing of 5 moving averages are respectively as shown by the following formula：
● 3 moving average formula of density：dden(i)=(dden(i-1)+dden(i)+dden(i+1))/3
Wherein, diRepresent the density value of some sampled point, di-1For the density value of the previous sampled point of the sampled point, di+1 For the density value of the latter sampled point of the sampled point；
● 5 moving average formula of density：dden(i)=(dden(i-2)+dden(i-1)+dden(i)+dden(i+1)+dden(i+2))/5
Wherein, diRepresent the density value of certain sampled point, di-2For the density value of the first two sampled point of the sampled point, di-1For The density value of the previous sampled point of the sampled point, di+1For the density value of the latter sampled point of the sampled point, di+2Adopted for this The density value of latter two sampled point of sampling point；
3 points of sonic data or the processing of 5 moving averages are respectively as shown by the following formula：
● 3 moving average formula of sound wave：dson(i)=(dson(i-1)+dson(i)+dson(i+1))/3
Wherein, diRepresent the sound wave value of some sampled point, di-1For the sound wave value of the previous sampled point of the sampled point, di+1 For the sound wave value of the latter sampled point of the sampled point；
● 5 moving average formula of sound wave：dson(i)=(dson(i-2)+dson(i-1)+dson(i)+dson(i+1)+dson(i+2))/5
Wherein, diRepresent the sound wave value of certain sampled point, di-2For the sound wave value of the first two sampled point of the sampled point, di-1For The sound wave value of the previous sampled point of the sampled point, di+1For the sound wave value of the latter sampled point of the sampled point, di+2Adopted for this The sound wave value of latter two sampled point of sampling point；
Step (2) 1. in, during standardization using the logging nomalize modules of geoview softwares at Reason, same codomain scope is normalized to by the sound wave of the well for establishing initial model and density data；
2. establish inverting initial model using the STRATA modules of geoview softwares；
Step (2) 2. in, when establishing inverting initial model：
When carrying out Inversion Calculation using the STRATA modules of geoview softwares, the setting of inverted parameters is：10-15Hz High cut-off frequency；Iterative times 10~20 times；Sample rate is 1ms~2ms；Maximum resistance variation scope is 25%~50%；Prewhitening Rate is 1%；Computing block size is 1ms~2ms, and the computing block size is identical with sample rate；Scale factor is 1；
Inverting type is multiple tracks inverting, Inline directions 10~20, Xline directions 10~20；
3. wave impedance inversion calculates：The 3-D seismics pure wave data obtained to step (1) carry out Inversion Calculation, obtain three-dimensional Wave Impedance Data Volume；
4. determine the threshold value of wave resistance anti-rotation lithology：The threshold value of the wave resistance anti-rotation lithology in each research area is not quite similar, The determination of threshold value is analyzed based on work area petrophysical parameter；
Step (2) 4. in, the cross plot modules of petrophysical parameter analysis and utilization geoview softwares.
5. lithology data calculates：The threshold value of the wave resistance anti-rotation lithology 4. obtained based on step (2), by step (2) 3. To three-dimensional Wave Impedance Data Volume be converted to three-dimensional lithology data body, the form of three-dimensional lithology data body is LITH (x, y, t), its Middle x represents No. Inline of 3D seismic data, and y represents No. Xline of 3D seismic data, and t represents the time；
The value of LITH (x, y, t) data volume is 0 or 1,0 expression mud stone, and 1 represents sandstone.
(3) the sand factor distributed data of destination layer position sand body is asked for, and is depicted as isogram；
The three-dimensional lithology data body obtained using step (2), mesh is calculated using the trace math modules of geoview softwares The sand factor data on stratum are marked, and draw plane equivalence；
Comprise the following steps that：
1. 3-D seismics pure wave data input Landmark seismic interpretation softwares are based on, the dimensionally number of plies that explanation is obtained According to importing in geoview softwares, the three-dimensional lithology data body of step (2) is imported into geoview softwares；
2. determining destination layer scope, using the trace math modules of geoview softwares in the range of the bottom of destination layer top, enter Row circle statistics calculate, and obtain stratum sand factor distributed data RATIO (x, y)；
3. using, " trace math " modules need to write code, and specific code is as follows：
Sand factor RATIO (x, y) distributed data of formation at target locations is can obtain accordingly；
4. to step (3) 2. in sand factor RATIO (x, y) data, carry out isopleth into figure.
(4) relation of porosity data and Acoustic Impedance Data is asked for
1. there are porosity data, sonic data, the situation of the class log data of density data three in area's log is studied Under：The intersection that porosity and wave impedance are carried out using the cross plot modules of excel softwares or geoview softwares is analyzed；
During analysis, the product that x transverse axis is Acoustic Impedance Data, Acoustic Impedance Data=sound wave and density data is set；The y longitudinal axis is Porosity data；Then fitted by the linear fit instrument of excel softwares or the cross plot modules of geoview softwares The change type y=ax+b, y of wave impedance turn hole porosity are porosity data, and x is Acoustic Impedance Data, and a, b are the ginseng for needing to be fitted Number；
2. in the case where studying the non-porous porosity log data in area：Using following formula (1), wave impedance is converted into hole Porosity data：
Wherein, ACSolid skeletalRepresent the interval transit time of rock solid skeleton, ACFluidBetween expression blowhole during the sound wave of fluid Difference, IMP are Acoustic Impedance Data, and POR is porosity data；
(5) transformational relation for the wave impedance turn hole porosity asked for using step (4), porosity is converted to by Acoustic Impedance Data Data, obtain formation at target locations porosity data distribution, and drawing isoline figure；
Comprise the following steps that：
1. the conversion relational expression obtained using step (4), is calculated using the trace math modules of geoview softwares Porosity 3D data volume POR (x, y, t), x represent No. Inline of 3D seismic data, and y represents 3D seismic data No. Xline, t represents the time；
2. determining destination layer scope, using the trace math modules of geoview softwares in the range of the bottom of destination layer top, enter Row circle statistics calculate, and obtain formation at target locations average pore distributed data POR_AVA (x, y), and x represents 3D seismic data No. Inline, y represents No. Xline of 3D seismic data；
The code write using trace math modules is as follows：
3. porosity POR_AVA (x, y) distributed data of formation at target locations is can obtain accordingly；
4. to step (5) 3. in obtained porosity POR_AVA (x, y) data, carry out isopleth into figure.
(6) 3-D seismics pure wave data are utilized, extract 3 kinds of earthquake RMS amplitude, instantaneous phase, arc length seismic properties, Cluster analysis is carried out to this three kinds of seismic properties, constraints is established based on borehole data, regression fit goes out a kind of new earthquake Combinations of attributes；
Specifically, step (1) 3-D seismics pure wave data input into Landmark softwares, is determined into the position of target reservoir Put, 3 kinds of earthquake RMS amplitude, instantaneous phase, arc length seismic properties are extracted in each 20ms thickness up and down in this position, then to this Three kinds of seismic properties carry out cluster analysis；
Identify the more attribute constraint situations of different zones, the restraint condition in the practical application in Er'lian Basin sandstone-type uranium mining area For：Earthquake RMS amplitude property value is more than 25, instantaneous phase property value and is more than 0, arc length property value more than 7, returns intend accordingly A kind of data of new seismic properties combination are closed out, and are depicted as isogram accordingly.
(7) above-mentioned formation at target locations sand factor data, porosity distributed data and seismic properties combination distribution are analyzed in intersection Data, integrated forecasting Formation of Sandstone-type Uranium Deposits beneficial zone；
Integrated forecasting Formation of Sandstone-type Uranium Deposits beneficial zone refers to numerical value in formation at target locations sand factor distribution map more than X's The region of region, porosity value more than Y, region of the seismic properties combined value more than Z carry out intersection analysis and research, carry out again accordingly Into the integrated forecasting of ore deposit beneficial zone.
X, the selection of Y, Z value is according to the different and different of sandstone-type uranium mineralization with respect area；The method for obtaining X, Y, Z value is by that will grind Study carefully all industrial bore positions in area to project respectively to formation at target locations sand factor distribution map, porosity distribution map, seismic properties In constitutional diagram, read the sand factor value, porosity value, seismic properties combined value of all bore positions, then by these sand factor values, Porosity value, seismic properties combined value carry out arithmetic average, obtain X, Y, Z value.
Integrated forecasting step is：
1. in the formation at target locations sand factor distribution map that step (3) obtains, region labeling of the codomain more than 0.8 is Favorable Areas A；
2. in the formation at target locations average pore distribution map that step (5) is obtained, region labeling of the codomain more than 12% is Favorable Areas B；
3. in the data isogram that the seismic properties that step (6) is obtained combine, codomain is more than 0.45 region labeling For Favorable Areas C；
4. overlapping above-mentioned tri- Favorable Areas of A, B, C using graphics software, the overlapping intersection area of three Favorable Areas of prediction is I Class ore prospect area；It is II classes into ore deposit Favorable Areas to have the overlapping intersection area of two panels Favorable Areas in three Favorable Areas of prediction；Other Situation does not give a forecast.

Claims (10)

  1. A kind of 1. sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method, it is characterised in that：This method comprises the following steps：
    (1) in research area, a set of sandstone-type uranium mineralization with respect 3-D seismics pure wave data are gathered；
    Field 3D seismic data is gathered, field 3D seismic data is handled successively to obtain 3-D seismics pure wave number According to；
    (2) Inversion Calculation is carried out to the 3-D seismics pure wave data of step (1) using based on modeling inversion, obtains three-dimensional wave resistance Anti- data volume, and then calculate three-dimensional lithology data body；
    (3) the sand factor distributed data of destination layer position sand body is asked for, and is depicted as isogram；
    The three-dimensional lithology data body obtained using step (2), using the trace math modules of geoview softwares with calculating target The sand factor data of layer, and draw plane equivalence；
    (4) relation of porosity data and Acoustic Impedance Data is asked for
    1. in the case where having porosity data, sonic data, the class log data of density data three in studying area's log：Make The intersection that porosity and wave impedance are carried out with the cross plot modules of excel softwares or geoview softwares is analyzed；
    During analysis, the product that x transverse axis is Acoustic Impedance Data, Acoustic Impedance Data=sound wave and density data is set；The y longitudinal axis is hole Degrees of data；Then wave resistance is fitted by the linear fit instrument of excel softwares or the cross plot modules of geoview softwares The change type y=ax+b, y of anti-rotation porosity are porosity data, and x is Acoustic Impedance Data, and a, b are the parameter for needing to be fitted；
    2. in the case where studying the non-porous porosity log data in area：Using following formula (1), wave impedance is converted into porosity Data：
    Wherein, ACSolid skeletalRepresent the interval transit time of rock solid skeleton, ACFluidThe interval transit time of fluid between expression blowhole, IMP is Acoustic Impedance Data, and POR is porosity data；
    (5) transformational relation for the wave impedance turn hole porosity asked for using step (4), the hole number of degrees are converted to by Acoustic Impedance Data According to, obtain formation at target locations porosity data and be distributed, and drawing isoline figure；
    (6) 3-D seismics pure wave data, 3 kinds of extraction earthquake RMS amplitude, instantaneous phase, arc length seismic properties, to this are utilized Three kinds of seismic properties carry out cluster analysis, establish constraints based on borehole data, regression fit goes out a kind of new seismic properties Combination；
    (7) above-mentioned formation at target locations sand factor data, porosity distributed data and seismic properties combination distributed data are analyzed in intersection, Integrated forecasting Formation of Sandstone-type Uranium Deposits beneficial zone；
    Integrated forecasting Formation of Sandstone-type Uranium Deposits beneficial zone refers to the area for being more than X to numerical value in formation at target locations sand factor distribution map The region of domain, porosity value more than Y, region of the seismic properties combined value more than Z carry out intersection analysis and research, carry out into again accordingly The integrated forecasting of ore deposit beneficial zone.
  2. A kind of 2. sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method as claimed in claim 1, it is characterised in that：In step (1), lead to Seismic detector collection field 3D seismic data is crossed, the process handled field 3D seismic data is to carry out quiet school successively Just, denoising, amplitude compensation, deconvolution, dynamic school superposition, migration processing.
  3. A kind of 3. sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method as claimed in claim 1, it is characterised in that：In step (2), tool Body step is as follows：
    1. the foundation of initial model：The well data in collection research area first, sound wave curve therein and density curve are carried out Smoothing processing and standardization；
    2. establish inverting initial model using the STRATA modules of geoview softwares；
    3. wave impedance inversion calculates：The 3-D seismics pure wave data obtained to step (1) carry out Inversion Calculation, obtain three-dimensional wave resistance Anti- data volume；
    4. determine the threshold value of wave resistance anti-rotation lithology：The threshold value of the wave resistance anti-rotation lithology in each research area is not quite similar, threshold The determination of value is analyzed based on work area petrophysical parameter；
    5. lithology data calculates：The threshold value of the wave resistance anti-rotation lithology 4. obtained based on step (2), step (2) is 3. obtained Three-dimensional Wave Impedance Data Volume is converted to three-dimensional lithology data body, and the form of three-dimensional lithology data body is LITH (x, y, t), wherein x No. Inline of 3D seismic data is represented, y represents No. Xline of 3D seismic data, and t represents the time；
    The value of LITH (x, y, t) data volume is 0 or 1,0 expression mud stone, and 1 represents sandstone.
  4. A kind of 4. sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method as claimed in claim 3, it is characterised in that：Step (2) 1. in, Use or 5 smoothing processings during smoothing processing at 3 points；
    3 points of density data or the processing of 5 moving averages are respectively as shown by the following formula：
    ● 3 moving average formula of density：dden(i)=(dden(i-1)+dden(i)+dden(i+1))/3
    Wherein, diRepresent the density value of some sampled point, di-1For the density value of the previous sampled point of the sampled point, di+1For this The density value of the latter sampled point of sampled point；
    ● 5 moving average formula of density：dden(i)=(dden(i-2)+dden(i-1)+dden(i)+dden(i+1)+dden(i+2))/5
    Wherein, diRepresent the density value of certain sampled point, di-2For the density value of the first two sampled point of the sampled point, di-1Adopted for this The density value of the previous sampled point of sampling point, di+1For the density value of the latter sampled point of the sampled point, di+2For the sampled point Latter two sampled point density value；
    3 points of sonic data or the processing of 5 moving averages are respectively as shown by the following formula：
    ● 3 moving average formula of sound wave：dson(i)=(dson(i-1)+dson(i)+dson(i+1))/3
    Wherein, diRepresent the sound wave value of some sampled point, di-1For the sound wave value of the previous sampled point of the sampled point, di+1For this The sound wave value of the latter sampled point of sampled point；
    ● 5 moving average formula of sound wave：dson(i)=(dson(i-2)+dson(i-1)+dson(i)+dson(i+1)+dson(i+2))/5
    Wherein, diRepresent the sound wave value of certain sampled point, di-2For the sound wave value of the first two sampled point of the sampled point, di-1Adopted for this The sound wave value of the previous sampled point of sampling point, di+1For the sound wave value of the latter sampled point of the sampled point, di+2For the sampled point Latter two sampled point sound wave value；
    Step (2) 1. in, handled using the logging nomalize modules of geoview softwares during standardization, will Sound wave and density data for the well of establishing initial model are normalized to same codomain scope；
    Step (2) 2. in, when establishing inverting initial model：
    When carrying out Inversion Calculation using the STRATA modules of geoview softwares, the setting of inverted parameters is：10-15Hz is high to be cut Frequently；Iterative times 10~20 times；Sample rate is 1ms~2ms；Maximum resistance variation scope is 25%~50%；Prewhitening rate is 1%；Computing block size is 1ms~2ms, and the computing block size is identical with sample rate；Scale factor is 1；
    Inverting type is multiple tracks inverting, Inline directions 10~20, Xline directions 10~20；
    Step (2) 4. in, the cross plot modules of petrophysical parameter analysis and utilization geoview softwares.
  5. A kind of 5. sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method as claimed in claim 1, it is characterised in that：In step (3), tool Body step is as follows：
    1. being based on 3-D seismics pure wave data input Landmark seismic interpretation softwares, it will explain that obtained three-dimensional formation data are led Enter in geoview softwares, the three-dimensional lithology data body of step (2) is imported into geoview softwares；
    2. determining destination layer scope, using the trace math modules of geoview softwares in the range of the bottom of destination layer top, followed Ring statistics calculates, and obtains stratum sand factor distributed data RATIO (x, y)；
    3. using, " trace math " modules need to write code, and specific code is as follows：
    Sand factor RATIO (x, y) distributed data of formation at target locations is can obtain accordingly；
    4. to step (3) 2. in sand factor RATIO (x, y) data, carry out isopleth into figure.
  6. A kind of 6. sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method as claimed in claim 1, it is characterised in that：In step (5), tool Body step is as follows：
    1. the conversion relational expression obtained using step (4), hole is calculated using the trace math modules of geoview softwares 3D data volume POR (x, y, t) is spent, x represents No. Inline of 3D seismic data, and y represents the Xline of 3D seismic data Number, t represents the time；
    2. determining destination layer scope, using the trace math modules of geoview softwares in the range of the bottom of destination layer top, followed Ring statistics calculates, and obtains formation at target locations average pore distributed data POR_AVA (x, y), and x represents 3D seismic data No. Inline, y represents No. Xline of 3D seismic data；
    The code write using trace math modules is as follows：
    3. porosity POR_AVA (x, y) distributed data of formation at target locations is can obtain accordingly；
    4. to step (5) 3. in obtained porosity POR_AVA (x, y) data, carry out isopleth into figure.
  7. A kind of 7. sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method as claimed in claim 1, it is characterised in that：, will in step (6) Step (1) 3-D seismics pure wave data input into Landmark softwares, determines the position of target reservoir, each up and down in this position 3 kinds of extraction earthquake RMS amplitude, instantaneous phase, arc length seismic properties in 20ms thickness, then this three kinds of seismic properties are carried out Cluster analysis；
    The more attribute constraint situations of different zones are identified, restraint condition is in the practical application in Er'lian Basin sandstone-type uranium mining area： Earthquake RMS amplitude property value is more than 25, instantaneous phase property value and is more than 0, arc length property value more than 7, and regression fit goes out accordingly A kind of data of new seismic properties combination, and isogram is depicted as accordingly.
  8. A kind of 8. sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method as claimed in claim 1, it is characterised in that：In step (7), X, Y, the selection of Z values is according to the different and different of sandstone-type uranium mineralization with respect area；The method for obtaining X, Y, Z value is the institute by that will study in area There is industrial bore position to project respectively into formation at target locations sand factor distribution map, porosity distribution map, seismic properties constitutional diagram, read Take the sand factor value, porosity value, seismic properties combined value of all bore positions, then by these sand factor values, porosity value, Shake combinations of attributes value and carry out arithmetic average, obtain X, Y, Z value.
  9. A kind of 9. sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method as claimed in claim 1, it is characterised in that：It is comprehensive in step (7) Closing prediction steps is：
    1. in the formation at target locations sand factor distribution map that step (3) obtains, region labeling of the codomain more than 0.8 is Favorable Areas A；
    2. in the formation at target locations average pore distribution map that step (5) is obtained, region labeling of the codomain more than 12% is favourable Area B；
    3. in the data isogram that the seismic properties that step (6) is obtained combine, region labeling of the codomain more than 0.45 is to have Sharp area C；
    4. overlap above-mentioned tri- Favorable Areas of A, B, C using graphics software, the overlapping intersection areas of three Favorable Areas of prediction for I classes into Ore deposit prospective area；It is II classes into ore deposit Favorable Areas to have the overlapping intersection area of two panels Favorable Areas in three Favorable Areas of prediction；Other situations Do not give a forecast.
  10. A kind of 10. sandstone-type uranium mineralization with respect Comprehensive Seismic Prediction method as claimed in claim 1, it is characterised in that：
    In step (1), field 3D seismic data, the process handled field 3D seismic data are gathered by seismic detector It is to carry out static correction, denoising, amplitude compensation, deconvolution, dynamic school superposition, migration processing successively；
    In step (2), comprise the following steps that：
    1. the foundation of initial model：The well data in collection research area first, sound wave curve therein and density curve are carried out Smoothing processing and standardization；
    Use or 5 smoothing processings during smoothing processing at 3 points；
    3 points of density data or the processing of 5 moving averages are respectively as shown by the following formula：
    ● 3 moving average formula of density：dden(i)=(dden(i-1)+dden(i)+dden(i+1))/3
    Wherein, diRepresent the density value of some sampled point, di-1For the density value of the previous sampled point of the sampled point, di+1For this The density value of the latter sampled point of sampled point；
    ● 5 moving average formula of density：dden(i)=(dden(i-2)+dden(i-1)+dden(i)+dden(i+1)+dden(i+2))/5
    Wherein, diRepresent the density value of certain sampled point, di-2For the density value of the first two sampled point of the sampled point, di-1Adopted for this The density value of the previous sampled point of sampling point, di+1For the density value of the latter sampled point of the sampled point, di+2For the sampled point Latter two sampled point density value；
    3 points of sonic data or the processing of 5 moving averages are respectively as shown by the following formula：
    ● 3 moving average formula of sound wave：dson(i)=(dson(i-1)+dson(i)+dson(i+1))/3
    Wherein, diRepresent the sound wave value of some sampled point, di-1For the sound wave value of the previous sampled point of the sampled point, di+1For this The sound wave value of the latter sampled point of sampled point；
    ● 5 moving average formula of sound wave：dson(i)=(dson(i-2)+dson(i-1)+dson(i)+dson(i+1)+dson(i+2))/5
    Wherein, diRepresent the sound wave value of certain sampled point, di-2For the sound wave value of the first two sampled point of the sampled point, di-1Adopted for this The sound wave value of the previous sampled point of sampling point, di+1For the sound wave value of the latter sampled point of the sampled point, di+2For the sampled point Latter two sampled point sound wave value；
    Handled during standardization using the logging nomalize modules of geoview softwares, will be used to establish initially The sound wave and density data of the well of model are normalized to same codomain scope；
    2. establish inverting initial model using the STRATA modules of geoview softwares；
    When carrying out Inversion Calculation using the STRATA modules of geoview softwares, the setting of inverted parameters is：10-15Hz is high to be cut Frequently；Iterative times 10~20 times；Sample rate is 1ms~2ms；Maximum resistance variation scope is 25%~50%；Prewhitening rate is 1%；Computing block size is 1ms~2ms, and the computing block size is identical with sample rate；Scale factor is 1；
    Inverting type is multiple tracks inverting, Inline directions 10~20, Xline directions 10~20；
    3. wave impedance inversion calculates：The 3-D seismics pure wave data obtained to step (1) carry out Inversion Calculation, obtain three-dimensional wave resistance Anti- data volume；
    4. determine the threshold value of wave resistance anti-rotation lithology：The threshold value of the wave resistance anti-rotation lithology in each research area is not quite similar, threshold The determination of value is analyzed based on work area petrophysical parameter；
    The cross plot modules of petrophysical parameter analysis and utilization geoview softwares；
    5. lithology data calculates：The threshold value of the wave resistance anti-rotation lithology 4. obtained based on step (2), step (2) is 3. obtained Three-dimensional Wave Impedance Data Volume is converted to three-dimensional lithology data body, and the form of three-dimensional lithology data body is LITH (x, y, t), wherein x No. Inline of 3D seismic data is represented, y represents No. Xline of 3D seismic data, and t represents the time；
    The value of LITH (x, y, t) data volume is 0 or 1,0 expression mud stone, and 1 represents sandstone；
    In step (3), comprise the following steps that：
    1. being based on 3-D seismics pure wave data input Landmark seismic interpretation softwares, it will explain that obtained three-dimensional formation data are led Enter in geoview softwares, the three-dimensional lithology data body of step (2) is imported into geoview softwares；
    2. determining destination layer scope, using the trace math modules of geoview softwares in the range of the bottom of destination layer top, followed Ring statistics calculates, and obtains stratum sand factor distributed data RATIO (x, y)；
    3. using, " trace math " modules need to write code, and specific code is as follows：
    Sand factor RATIO (x, y) distributed data of formation at target locations is can obtain accordingly；
    4. to step (3) 2. in sand factor RATIO (x, y) data, carry out isopleth into figure；
    In step (5), comprise the following steps that：
    1. the conversion relational expression obtained using step (4), hole is calculated using the trace math modules of geoview softwares 3D data volume POR (x, y, t) is spent, x represents No. Inline of 3D seismic data, and y represents the Xline of 3D seismic data Number, t represents the time；
    2. determining destination layer scope, using the trace math modules of geoview softwares in the range of the bottom of destination layer top, followed Ring statistics calculates, and obtains formation at target locations average pore distributed data POR_AVA (x, y), and x represents 3D seismic data No. Inline, y represents No. Xline of 3D seismic data；
    The code write using trace math modules is as follows：
    3. porosity POR_AVA (x, y) distributed data of formation at target locations is can obtain accordingly；
    5. to step (5) 3. in obtained porosity POR_AVA (x, y) data, carry out isopleth into figure；
    In step (6), step (1) 3-D seismics pure wave data input into Landmark softwares, is determined into the position of target reservoir Put, 3 kinds of earthquake RMS amplitude, instantaneous phase, arc length seismic properties are extracted in each 20ms thickness up and down in this position, then to this Three kinds of seismic properties carry out cluster analysis；
    The more attribute constraint situations of different zones are identified, restraint condition is in the practical application in Er'lian Basin sandstone-type uranium mining area： Earthquake RMS amplitude property value is more than 25, instantaneous phase property value and is more than 0, arc length property value more than 7, and regression fit goes out accordingly A kind of data of new seismic properties combination, and isogram is depicted as accordingly.
    In step (7), the selection of X, Y, Z value is according to the different and different of sandstone-type uranium mineralization with respect area；The method for obtaining X, Y, Z value is logical Cross by study area in all industrial bore positions project respectively to formation at target locations sand factor distribution map, porosity distribution map, Shake in combinations of attributes figure, read the sand factor value, porosity value, seismic properties combined value of all bore positions, then these are contained Sand coarse aggregate ratio value, porosity value, seismic properties combined value carry out arithmetic average, obtain X, Y, Z value；
    Integrated forecasting step is：
    1. in the formation at target locations sand factor distribution map that step (3) obtains, region labeling of the codomain more than 0.8 is Favorable Areas A；
    2. in the formation at target locations average pore distribution map that step (5) is obtained, region labeling of the codomain more than 12% is favourable Area B；
    3. in the seismic properties data splitting isogram that step (6) is obtained, region labeling of the codomain more than 0.45 is favourable Area C；
    4. overlap above-mentioned tri- Favorable Areas of A, B, C using graphics software, the overlapping intersection areas of three Favorable Areas of prediction for I classes into Ore deposit prospective area；It is II classes into ore deposit Favorable Areas to have the overlapping intersection area of two panels Favorable Areas in three Favorable Areas of prediction；Other situations Do not give a forecast.
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Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108897041A (en) * 2018-08-16 2018-11-27 中国石油天然气股份有限公司 Prediction method and device for uranium ore enrichment area
CN109165436A (en) * 2018-08-17 2019-01-08 中国地质科学院探矿工艺研究所 Method for measuring source quantity of modern moraine type marine glacier
CN111190240A (en) * 2020-01-13 2020-05-22 吉林大学 Method for extracting ore-forming structural elements of sandstone-type uranium ores based on three-dimensional seismic interpretation
CN111239815A (en) * 2020-01-20 2020-06-05 吉林大学 Sandstone-type uranium reservoir mineralization and deposition element extraction method based on three-dimensional seismic attributes
CN111257926A (en) * 2018-12-03 2020-06-09 核工业二0八大队 Method for predicting ancient valley uranium reservoir by using old seismic data
CN111852467A (en) * 2020-07-28 2020-10-30 核工业北京地质研究院 Method and system for delineating extension range of sandstone uranium ore body
CN111983721A (en) * 2020-08-26 2020-11-24 核工业北京地质研究院 Sandstone uranium ore mud-sand-mud geological structure identification method and system
CN112764122A (en) * 2019-10-21 2021-05-07 中国石油化工股份有限公司 Delineation method of ancient river channel type sandstone uranium reservoir
CN113514886A (en) * 2021-07-22 2021-10-19 核工业北京地质研究院 Geological-seismic three-dimensional prediction method for beneficial part of sandstone-type uranium deposit mineralization
CN113640873A (en) * 2021-08-18 2021-11-12 核工业二0八大队 Sandstone-type uranium ore prestack earthquake prediction method and device

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2638312A1 (en) * 2007-07-26 2009-01-26 Shale And Sands Oil Recovery Llc Method and process for the systematic exploration of uranium in the athabasca basin
CN103954995A (en) * 2014-04-22 2014-07-30 核工业北京地质研究院 Sand body reorganization method in sandstone-type uranium deposit exploration
CN104656162A (en) * 2015-02-13 2015-05-27 西安石油大学 Method for determining content of uranium and organic carbon in uranium-rich hydrocarbon-source rocks
CN105403909A (en) * 2015-08-13 2016-03-16 核工业北京地质研究院 Method for detecting paleochannel type uranium mine

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2638312A1 (en) * 2007-07-26 2009-01-26 Shale And Sands Oil Recovery Llc Method and process for the systematic exploration of uranium in the athabasca basin
CN103954995A (en) * 2014-04-22 2014-07-30 核工业北京地质研究院 Sand body reorganization method in sandstone-type uranium deposit exploration
CN104656162A (en) * 2015-02-13 2015-05-27 西安石油大学 Method for determining content of uranium and organic carbon in uranium-rich hydrocarbon-source rocks
CN105403909A (en) * 2015-08-13 2016-03-16 核工业北京地质研究院 Method for detecting paleochannel type uranium mine

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
吴曲波，等: "砂岩型铀矿地震勘探技术应用现状与发展", 《物探与化探》 *
常树帅: "俄罗斯砂岩型铀矿成矿定量预测技术及应用", 《铀矿地质》 *
李子伟，等: "地震勘探方法在伊犁盆地南缘砂岩型铀矿勘查中的应用研究", 《铀矿地质》 *
李晓翠，等: "鄂尔多斯盆地南部砂岩型铀矿成矿预测", 《铀矿地质》 *
潘自强，等: "砂岩型铀矿勘探中的地震资料高分辨率处理方法", 《工程地球物理学报》 *

Cited By (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108897041A (en) * 2018-08-16 2018-11-27 中国石油天然气股份有限公司 Prediction method and device for uranium ore enrichment area
CN109165436A (en) * 2018-08-17 2019-01-08 中国地质科学院探矿工艺研究所 Method for measuring source quantity of modern moraine type marine glacier
CN111257926A (en) * 2018-12-03 2020-06-09 核工业二0八大队 Method for predicting ancient valley uranium reservoir by using old seismic data
CN111257926B (en) * 2018-12-03 2022-07-26 核工业二0八大队 Method for predicting ancient valley uranium reservoir by using old seismic data
CN112764122B (en) * 2019-10-21 2023-12-12 中国石油化工股份有限公司 Method for confining uranium reservoir of ancient river channel sandstone
CN112764122A (en) * 2019-10-21 2021-05-07 中国石油化工股份有限公司 Delineation method of ancient river channel type sandstone uranium reservoir
CN111190240A (en) * 2020-01-13 2020-05-22 吉林大学 Method for extracting ore-forming structural elements of sandstone-type uranium ores based on three-dimensional seismic interpretation
CN111190240B (en) * 2020-01-13 2021-09-07 吉林大学 Method for extracting ore-forming structural elements of sandstone-type uranium ores based on three-dimensional seismic interpretation
CN111239815B (en) * 2020-01-20 2021-07-13 吉林大学 Sandstone-type uranium reservoir mineralization and deposition element extraction method based on three-dimensional seismic attributes
CN111239815A (en) * 2020-01-20 2020-06-05 吉林大学 Sandstone-type uranium reservoir mineralization and deposition element extraction method based on three-dimensional seismic attributes
CN111852467A (en) * 2020-07-28 2020-10-30 核工业北京地质研究院 Method and system for delineating extension range of sandstone uranium ore body
CN111983721A (en) * 2020-08-26 2020-11-24 核工业北京地质研究院 Sandstone uranium ore mud-sand-mud geological structure identification method and system
CN113514886A (en) * 2021-07-22 2021-10-19 核工业北京地质研究院 Geological-seismic three-dimensional prediction method for beneficial part of sandstone-type uranium deposit mineralization
CN113514886B (en) * 2021-07-22 2021-12-10 核工业北京地质研究院 Geological-seismic three-dimensional prediction method for beneficial part of sandstone-type uranium deposit mineralization
CN113640873A (en) * 2021-08-18 2021-11-12 核工业二0八大队 Sandstone-type uranium ore prestack earthquake prediction method and device
CN113640873B (en) * 2021-08-18 2024-04-12 核工业二0八大队 Sandstone uranium ore pre-stack earthquake prediction method and device

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